A generalized implicit enumeration algorithm for graph coloring
Communications of the ACM - Lecture notes in computer science Vol. 174
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Applied numerical linear algebra
Applied numerical linear algebra
Exact coloring of real-life graphs is easy
DAC '97 Proceedings of the 34th annual Design Automation Conference
New methods to color the vertices of a graph
Communications of the ACM
25 pretty graph colouring problems
Discrete Mathematics
Some simplified NP-complete problems
STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Detection using correlation bound in a linear mixture model
Signal Processing
Robust Gaussian and non-Gaussian matched subspace detection
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A Novel Approach for Target Detection and Classification Using Canonical Correlation Analysis
Journal of Signal Processing Systems
Hi-index | 35.68 |
Detection of a given target or set of targets from observed data is a problem countered in many applications. Regardless of the algorithm selected, detection performance can be severely degraded when the subspace defined by the target data set is singular or ill conditioned. High correlations between target components and their linear combinations lead to false positives and misidentifications, especially for subspace-based detectors. In this paper, we propose a subspace partitioning scheme that allows for detection to be performed in a number of better conditioned subspaces instead of the original subspace. The proposed technique is applied to Raman spectroscopic data analysis. Through both simulation and experimental results, we demonstrate the improvement in the overall detection performance when using the proposed subspace partitioning scheme in conjunction with several subspace detection methods that are commonly used in practice.